Tongue segmentation algorithm for traditional Chinese medicine based on convolutional neural network

Pengzhao Sun; Xiaoping Yang; Yuhong Ban

doi:10.1117/12.2542935

18 December 2019 Tongue segmentation algorithm for traditional Chinese medicine based on convolutional neural network

Pengzhao Sun, Xiaoping Yang, Yuhong Ban

Proceedings Volume 11338, AOPC 2019: Optical Sensing and Imaging Technology; 113380Z (2019) https://doi.org/10.1117/12.2542935
Event: Applied Optics and Photonics China (AOPC2019), 2019, Beijing, China

Abstract

Tongue diagnosis is an important part of the Traditional Chinese Medicine (TCM) diagnosis. In Chinese Medicine, tongue body reflects the most sensitive indicators of the physiological function and pathological changes, which has important application value in the process of diagnosis and treatment of the TCM diagnosis. The accurate separation of tongue body from tongue image is the premise of recognition and diagnosis. Most of the proposed tongue segmentation algorithms are based on the improvement of traditional approaches. These algorithms can improve the segmentation accuracy of tongue image to some extent, but they are less robust. To address above problems, a method of fast tongue image segmentation algorithm using convolutional neural network is proposed in this paper. The network is inspired from ShuffleNet which provided an efficient classification and detection network. The running time of our structure is about 0.16s, and the average segmentation precision is about 90.5%, which makes it of great potential for real-time applications. As opposed to the two common traditional segmentation methods (Kmeans++, GrabCut), the proposed method performs better than the above algorithms.

Citation Download Citation

Pengzhao Sun, Xiaoping Yang, and Yuhong Ban "Tongue segmentation algorithm for traditional Chinese medicine based on convolutional neural network", Proc. SPIE 11338, AOPC 2019: Optical Sensing and Imaging Technology, 113380Z (18 December 2019); https://doi.org/10.1117/12.2542935

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